Management Decision Support Systems

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Table of contents

Introduction. 3

Review.. 3

Characteristics and Capabilities of Decision Support Systems. 5

History and Development of Decision Support Systems. 6

Components/Subsystems. 8

Data management component 8

Model management component 9

Knowledge management component 9

User interface component 10

Classification of Decision Support Systems. 10

Types of Decision Support Systems. 12

Techniques and Approaches. 13

Benefits of Decision Support Systems. 13

Limitations of Decision Support Systems. 14

Applications. 15

Conclusion. 17

 

 

 

 

 

 

Management Decision Support Systems

Introduction

Managers often handle many activities in the course of the day. They have to solve many problems as they handle daily operations. Some of the challenges they encounter are familiar, as they tend to happen often. Therefore, the management has already established a system of handling such problems. The manager follows the identified procedures when solving the problems. However, other problems do not have obvious solutions. They happen abruptly and the manager might not have experience in solving them. This means that he has to spend more time trying to find the most effective solutions. Management decision support systems enable the managers to solve problems and work more effectively. Technology has benefited managers because it has made it possible for them to solve the problems they encounter with relative ease. Managers do not have to have technical knowledge so that they can use the decision support systems. The use of management decision support systems has benefited managers who have to deal with increasingly complex situations and many responsibilities.

Review

Management decision support systems are computer-based applications that managers use to make decisions. They not only provide information but they incorporate people, relevant software, necessary procedures and databases to help, which enables the managers to use them when solving problems. Managers are able to analyze the data they have and the systems interactive function gives them the information they need to make decisions. Managers prefer decision support systems to management information systems because they have the ability to handle unstructured problems. In addition, the managers are free to make the decisions they see fit regardless of the results they get from the decision support system. The support systems have querying capabilities and they give the managers several options. Managers at different levels, individuals as well as groups can use the decision support systems. The support systems are flexible and adaptable to different situations. Companies can use them irrespective of the managers’ leadership style or the process they use when making decisions.

Some managers reported unsatisfactory results when using the management decision support systems. The development of some of the systems did not consider both the technical and problem solving aspect of the system. The focus on the technical aspect of the system meant that people with less technical knowledge could not use the support system effectively. The effectiveness of the support systems will depend on the database management system and other inputs. This will determine the experiences that the user will have when using the support system. If the research models, statistics or the database management do not have correct or adequate information, then the decision support system will not benefit the management much. The condition of the database management system determines the effectiveness of the support system, as it contains all the information.

Successful decision support systems are interactive. The users are able to use the system well without encountering challenges. The system works well with other databases. Effective systems are able to detect any changes that occur. The systems have good communication capabilities. They have a detection system, which enables them to notice any errors that the users make. Effective systems use different applications to transform data into meaningful information. The development and continual advancement of the decision support systems has led to the additional capabilities of the system to store voluminous amount of data. This can lead to data overload, which can lead to difficulties in decision-making process. An effective decision support system is able to use different applications to extract the most relevant data, thereby assisting the managers to make relevant decisions. The systems are able to analyze historical information and trends and this enables it to make predictions of future performance (Chee & Jain, 2010).

Project Discussion

Characteristics and Capabilities of Decision Support Systems

Decision support systems assist in solving unstructured and semi structured problems. Managers at every level and with different levels of experience can use the systems. The systems should be able to respond quick enough to reflect the managers’ decision-making capabilities. The system can be used by individuals as well as groups. They are interactive, user friendly, and easy to use. They differ from other information systems because the users control the process. They have the freedom to make their own decisions although the system makes different suggestions. This is because the system is a form of support and not a replacement. The user has control at all levels in the process of making decisions (Power, 2013).

The systems are flexible and users can manipulate them to suit their requirements. In addition, users can modify simple systems to suit their preferences. Many applications developed and the use of web-based technologies has made it possible for users to develop large decision support systems. The flexibility of the systems enables them to be used by an individual user or to be distributed throughout the organization. The system can also be used with other similar systems and applications. They support decisions made repeatedly as well as decisions that are only made once.

They provide support at all levels of decision-making process including design and implementation. A distinguishing characteristic of the decision support systems is their use of models for analysis (Schuff et al., 2010). This enables the user to experiment with different strategies and decisions before making a decision. It reduces the chance of error and associated costs. The systems make it possible for users to access different sources and formats of data. They contain huge amounts of different types of data, which includes graphics and multimedia.

History and Development of Decision Support Systems

The concept of management decision support systems began in the 1960s when many people were beginning to look at different ways of using the computer to ease different management processes in the organization. However, initial development of the decision support system may have begun in the 1950s as people worked with interactive computer systems (Power, 2013). They began using information systems at the time to perform different operational transactions such as payroll and order processing. Researchers intended to use computerized systems to assist organizations to make decisions. Progress was made by Scott Morton who researched on the possibility of using computerized systems for managerial decision making in the seventies. He examined how managers could use the system and analysis models to make decisions, yet still have the power to make their own judgment. He focused their research on decision making in the organization as well as the interactivity of the computer systems.

Other researchers such as T. P. Gerrity and Gordon Davis worked to advance and develop the design support systems. Gerrity focused on how investment managers could use the support system to manage their clients stocks. Davis concentrated on management information systems and looked at how managers could use them to make decisions. His work helped to lay a foundation for management decision support systems as it looked at diverse issues in management and computer technology. J. C. Little was also instrumental in developing DSS. He identified the standards and conditions that people could use when developing models for making decisions in management. He asserted that the models had to be robust, simple, and easy to control, and have relevant details to make decisions. These standards are instrumental in designing DSS. Ralph Sprague and Eric Carlson provided practical ways of how organizations could build DSS. They recognized the ability of the DSS to solve semi structured and unstructured problems (Power, 2013).

Many researchers gained an interest in the support systems, and they furthered their research in the area. This led to the development of other systems based on the DSS model, including the group decision support system, executive information systems, and organizational decision support system in the 80s. Other developments included online analytical processing, software in data warehousing and applications in business intelligence in the 90s. As more people acquired personal computers, focus shifted away from mainframe to client based or server based decision support systems. Other developments in the 90s included the use of object-oriented technology, completion of data warehouses, and use of online analytical processing in databases. The development of the internet led to further changes in DSS as many people began using online-based applications.

Today, people are able to build individualized decision support systems. They use tempos, modules that are user programmed and other features. However, many businesses opt to purchase intricate systems to gain maximum benefits. The design of the decision support systems has changed over the years, reflecting the changes in technology and business needs. For instance, modern programs and applications consider the fact that businesses need to make fast and effective decisions, and this has led to development of facilities with more storage capacities as well as processors with increased speeds. The changes and improvement of DSS has made it cheaper and easier to store, process, and retrieve information.

Components/Subsystems

Data Management Component

Decision support systems have different components or subsystems. The manager uses the dialog management component to communicate. The user interface then communicates with the database management and the model based component. The function of the data management component is to store the information needed by the decision support system. Data can come from internal or external sources. It can be in different formats such as numbers, videos, words, and images. Only data that can be transformed into information should be included in the database (Sauter, 2011). It has to be organized in the correct manner to enable easy retrieval. Feeding the system with the wrong data will yield the wrong results irrespective of the care taken in other procedures. This will in turn resort to the manager making the wrong decisions. One has to decide the information that will be of value to the managers when making decisions.

The subsystem consists of the database management system (DBMS) as it also maintains all the information used by the decision support system. The DBMS software makes it possible to manage the data. The subsystem consists of a query facility and a data directory. The user can manipulate and query the data as well as remove and modify the data as required. In addition, the query facility receives data from other components in the system and it decides how to fill the results and return them to the components that had issued the requests.

The data directory is a catalog containing all the data and definitions of different items. It determines the availability and location of the data. It provides the necessary support to receive new items of data, delete those that are not required, and retrieve information. The changing nature of business has led to changes in data required for decision support systems. As businesses become more complex, managers need more information to make decisions. They need data from traditional information sources such as customers and employees and they need to be aware of any trends and other activities around the world. These data needs to be fed in the system and it will enable the managers to make informed decisions (Sauter, 2011).

Model Management Component

The main function of the model subsystem is to change the data contained in the database management system into information, which can be used to make decisions. This component monitors all the models used during the analysis and all the controls that will be required for operating the models. It consists of both qualitative and quantitative models, such as finance and statistics. The subsystem helps the user to build models. The users can determine whether the models suggested are the most appropriate for making decisions. The support system incorporates and links models from different disciplines, which will be used for analysis. This subsystem contains all the models required to make effective analysis (Sauter, 2011).

It enables the creation and maintenance of different models. The user can decide the model to use by accessing the external interface. The subsystem has a model base, which contains quantitative models. The models can be used for analyses, formulation of strategy, and operations. The subsystem has mobile languages such as Java and online analytical processing and others, which make it possible for users to create models. The model directory consists of a catalog with all the information on models and software.

Knowledge Management Component

The knowledge management component acts as an expert system. It contains all the information and offers suggestions on how to solve the problems that the user may be experiencing. It is especially necessary when dealing with complex problems, which may require expert decisions. Therefore, it is present in many decision support systems, which tend to deal with unstructured problems that are often complicated in nature. It analyses information from different sources and offers the most suitable alternatives to the managers based on the problem. It is an optional component and it supports the other subsystems. The component consists of intelligent systems including expert systems, intelligent agents, and neural networks among others.

User Interface Component

The user interface component is a dialog management and it enables communication between the user and the system. The person making the decision supplies the system with information and commands it on the tasks to perform. The user interface should be easy to use. Many managers do not have technical knowledge but they should be able to use the system. The user interface should not use jargon to facilitate effective communication and interaction, which will enable the manager to understand recommendations from the system. Many user interface components include features such as icons and menus, which make it easy for the users to navigate the system. In addition, users can seek help by accessing different features and applications present in the component. An effective user interface component interacts with the user. It is able to foresee the action that the user will perform next and it uses simple language for communication. It notifies the users when they make an error

Classification of Decision Support Systems

Researchers have come up with different ways of classifying decision support systems. This is because of the differences in the support systems. Some tend to focus on the data, others on the models, while other systems focus on communication. In addition, some systems are meant for individual use and others are customized for group use. Steven Alter identified seven classifications of DSS, which include the file drawer systems, analysis information systems, data analysis systems, representational models, suggestion models, accounting and financial models, and optimization models. The file drawer systems make it possible to access the data. The user can access some models and databases through the analysis information system. The system facilitates the analysis of data. Decision makers can manipulate data by using customized or general tools contained in the data analysis systems. Representational models use the simulation models to approximate the repercussions of taking a particular action and they identify any underlying relationships. The suggestion models consist of the descriptive and prescriptive models that are used to make recommendations on the best option to take. The optimization models approximate the consequences of the choices available to make decisions.

Holsapple and Whinston identified six DSS classifications, which include text, database, spreadsheet, solver, and rule oriented DSS as well as compound DSS. The compound DSS combines two or more of the other frameworks. The text-oriented DSS gets text information from different sources such as reports and statements. The information is arranged based on the expected use and the type of decision that will likely be made. Database-oriented DSS considers the voluminous nature of data when structuring information. Information is arranged in a way that will be easy to retrieve when required. The spreadsheet-oriented DSS uses spreadsheet applications to store information and it is useful when changes occur in the organizations. The solver-oriented DSS uses programs for solving quantitative problems. The rule-oriented DSS uses artificial intelligence standards and it is useful for expert systems.

Hackathon and Keen came up with three categories of DSS based on the number of the people using the system. The categories include organizational, group, and personal. Donovan and Madnick used the nature of the situation to categorize DSS. They identified two taxonomies, which are institutional and ad hoc. Institutional DSS is used often as it supports recurring decisions while ad hoc DSS support unexpected problems. Power classified DSS based on data, knowledge, model, document, and communication (Power, 2013)

Types of Decision Support Systems

Executive information system is a form of decision support system that specifically focuses on senior management. It focuses on the most critical data of the organization, which tends to be the responsibility of the executive management. It is designed for specific individuals and it is easy to use. It offers support for different managerial functions such as planning and organizing, defining the vision for the organization, management in time of crisis, strategic organization and control, and staffing. Managers are able to manipulate and modify the information in real time, and they avoid any delays in the process. They have access to different systems within the organization and this enables them to make relevant and timely decisions. Many developers today take advantage of the internet and they develop applications that run on web browsers. The availability of software and the internet has reduced the cost of the executive information systems.

Many managers encourage and implement the use of teams in the organizations. Teamwork is essential for the accomplishment of different projects in the company. The group decision support system is a form of decision support system designed for team decision making. It is useful for groups that share similar responsibilities. It differs from the management decision support system by incorporating facilities that make it possible for the group members to communicate with each other. In addition, this form of support system can be used in different ways depending on the context. The group can use the decision support system when it is meeting. The ability to use a centralized system means that the group does not have to meet. The members can access the system and perform common duties.

Techniques and Approaches

DSS incorporates the use of different techniques including case based reasoning, neural networks, and fuzzy query and analysis. Case based reasoning is based on the experiences that the user has in solving problems. The decision maker uses the old approach to solve emerging problems. This approach does not require the user to have complete data because it is not specific. The neural networks appear in the form of computer models containing massive numbers of neurons that are connected to each other. They can be supervised in the sense that they will use the input and target data samples. The use of the target data act as the supervising signals and they assist in correcting any forecasts made. Unsupervised neural networks only use input samples. The fuzzy query and analysis approach deal with uncertainties and incomplete information. It is based on real situations, which are often complicated and require different approaches (Chee & Jain, 2010).

Benefits of Decision Support Systems

Many organizations have realized the benefits of incorporating management decision support systems. The systems improve the quality of the decisions made, as it considers many factors. Relying on human beings alone to make the decisions may not be as effective. They may be facing many obstacles and challenges at the time, which will limit their ability to make the right decisions. Good decisions benefit the company in different ways. They enable the organizations to prevent losses and reduce business risks. The relationship that the company has with different stakeholders such as customers and suppliers also improves when the management continually makes good decisions. Moreover, having a good management decision support system enables the company to improve the customer services and this means that the company will have a better relationship with the customers. The decision support systems also help to save on resources and reduce costs. Managers spend less time making decisions, and this means that they can focus on other things. Hence, the support systems also help to increase productivity in the company. The managers are able to save time because the decision support systems are able to identify problems and solutions quickly.

Limitations of Decision Support Systems

Although decision support systems offer numerous benefits for companies and managers, they have some limitations, which have prevented some organizations from adopting them. The systems do not have aspects such as intuition or creativity, which are necessary for making ad hoc decisions. Situations change often and managers need to make decisions fast. They may not have the time to consult the system. Designers cannot incorporate all the data from different sources. For instance, it is not possible to record all the experiences that one has had when dealing with different situations. However, managers can depend on these experiences to make decisions without using the support systems. Another limitation of the support systems involves the hardware and software required. Using the wrong applications will have a negative effect on the decisions made, as the information may not be complete and accurate (Cioca & Cioca, 2010)

The changing nature of the business environment has necessitated the need to use management decision support systems. Current managers are working in a global environment, which requires them to make decisions quickly. They have to make fast and relevant decisions, and this will involve having the ability to recognize problems as they occur or to predict the existence of future challenges in their business. Managers are increasingly making complex decisions in their organizations and they cannot rely solely rely on human intuition and experience. They need scientific facts and analyses to enable them make good decisions. The increasing competitive nature of the business environment means that managers have to make accurate and immediate decisions. They do not have time to make and rectify mistakes because this means that they may lose opportunities (Power, 2013). Using analysis enables them to identify the customers they should target, the type of clients they have, and the most suitable methods they can use to market their products and services.

Managers are looking for ways to ensure that their companies have competitive advantage over their rivals. They will use the methods that will guarantee increased productivity and satisfaction and minimal but effective use of resources. Using management decision support systems reduces bias that may exist when making decisions. Managers can be prone to bias when they depend on their intuition and experience, as they will rely on historical data and information. Using the support system reduces bias as it places greater emphasis on relevant and recent information. This has encouraged many organizations to use decision support system for management purposes (Power, 2013). These factors have necessitated the use of decision support systems.

Applications

As new technologies and applications for making the DSS evolved, many people began using it for many purposes and they were not confined within an organization. Managers in every field can use the decision support systems to make decisions. They are applicable in the transportation and travel industry, where they are used to optimize traveling time for airlines and rail transport. They are also applicable in agriculture, management of natural resources, forestry and business management. In agriculture and farm management, the decision support systems assist in determining the rate and factors that affect crop growth. They enable farmers to incorporate scientific research and knowledge in their farming practice. Farmers are able to access agricultural services easily using the support systems (Agrahari & Tripathi, 2012).

Decision support systems are used in hospital and clinical setting, where they reduce errors and medical costs and improve medical safety. Doctors make medical errors when they make the wrong diagnosis, prescribe the wrong medication, order the wrong lab results, and failing to treat emergency cases urgently. The system can assist physicians by making the errors more visible and making recommendations for error reductions. The system used case based approaches because the health workers often deal with cases with similar characteristics. Effective systems developed in such settings contain accurate information. Health practitioners access knowledge through the system and this makes it possible for them to solve problems. The system alerts the physicians in case of error; they make recommendations for diagnosis, and they interpret and manage the results (Jao & Hier, 2010).

Decision support systems help in making decisions in complex and uncertain situations. This makes them suitable for disaster management. The ability to use historical data and analysis to predict different situations facilitates its use in controlling and managing natural disasters such as floods, storms, and earthquakes. Researchers are able to combine technology and information from meteorological institutions to know the factors that trigger disasters and the possible effects of the disasters in certain areas and this enables them to make informed decisions. they determine whether the people should move away from certain areas and the methods that they can use to mitigate the effects (Cioca & Cioca, 2010).

People have found ways of using decision support systems in environment management. They enable environmentalists and other policy makers to make informed decisions on resource management, identify the connection between different elements of the environment such as air, water and land, and use scientific facts and research to make decisions. it is useful for making decisions concerning biodiversity, pollution, and climate change among other environmental concerns. Decision support systems incorporate knowledge from different environmental disciplines. The flexibility and adaptability of the systems makes it possible for users to modify the system to suit their conditions. Users can concentrate on the environmental issues that are specific to their region. In addition, they are able to identify any cause and effect relationships. Environmentalists can observe and predict the consequences of environmental changes using the decision support systems. They can use their knowledge to influence changes in policy and regulations thus contributing to environmental preservation and conservation (Booty & Wong, 2010).

Conclusion

Many organizations have realized the importance of decision support systems for management functions. The support systems have several benefits as it enhances the decision making process. Managers make effective and relevant decisions using the system. The effectiveness of the support systems depends on the design. Designers should use the most advanced applications and relevant hardware to ensure that they have good systems and increase effectiveness. They should also be aware of managerial responsibilities and organizational needs as this will make the system more relevant. Failure to do so will lead to an ineffective design. Design support systems continue to evolve and they are used in many diverse applications. .

 

 

 

 

 

 

References

Agrahari, A., & Tripathi, S. (2012). A theoretical framework for development of decision support system for agriculture. International Journal of Engineering and Science, 1 (6), 50-55

Booty, W., & Wong, I. (2010). Case studies of Canadian environmental decision support systems. Retrieved from http://www.intechopen.com/books/decision-support-systems/case-studies-of-canadian-environmental-decision-support-systems

Chee, L. P., & Jain, C. L. (2010). Handbook on decision-making: Vol 1: Technologies and applications. New York, NY: Springer

Cioca, M., & Cioca, I. L. (2010). Decision support systems used in disaster management. Retrieved from http://www.intechopen.com/books/decision-support-systems/decision-support-systems-used-in-disaster-management

Jao, S. C., & Hier, B. D. (2010). Clinical decision support system: An effective pathway to reduce medical errors and improve patient safety. Retrieved from http://www.intechopen.com/books/decision-support-systems/clinical-decision-support-systems-an-effective-pathway-to-reduce-medical-errors-and-improve-patient-

Power, D. (2013). Decision support, analytics, and business intelligence, 2^nd ed. New York, NY: Business Expert Press

Sauter, L. V. (2011). Decision support systems for business intelligence. Hoboken, NJ: John Wiley & Sons

Schuff, D., Paradice, D., & Burstein, F. (2010). Decision support: An examination of the DSS discipline. New York, NY: Springer